Updating recipes

bioconda-utils now has the ability to automatically update packages and submit a PR on your behalf. Note that the auto-updater does not yet know how to monitor changed dependencies, so it is important to verify that the updated recipe reflects all the changes. This tool is most useful when scanning packages to know when something has been updated.

bioconda-utils autobump recipes/ config.yml --packages <my-package-name>

will update the package’s recipe which you can then inspect. If you have a GitHub personal access token set as the environment variable GITHUB_TOKEN, the following command will additionally submit a pull request on your behalf:

bioconda-utils autobump recipes/ config.yml \
  --packages <my-package-name> \

By default, subrecipes will be ignored for updating unless they specifically have the following in their extra block:

    enable: yes

E.g., packagename/meta.yaml is the main recipe, and packagename/0.5/meta.yaml is a subrecipe. So packagename/0.5/meta.yaml needs the above extra block if it should ever be automatically updated.

How it works

This section describes the system in more detail for developers looking to expand the functionality of the update scanner or anyone interested in the inner workings.

The components

The bioconda_utils.recipe.Recipe object contains logic for finding and replacing text that may contain version information (such as within package: and source: sections or Jinja set statements), for resetting build numbers, and for reading and writing recipes. Notably, reading and writing recipes here does not rely on conda-build as we need to do things conda-build was not designed for.

The bioconda_utils.autobump.Scanner class reads recipes and applies various filters (described below) in an asyncio loop to dramatically speed up the many http calls that are performed over the course of scanning for updates. It also handles other things like retrying after increasingly longer wait times when it encounters non-permanent HTTP errors.

Subclasses of bioconda_utils.autobump.Filter are added to the scanner. They contain logic in their apply method. This method at least takes a recipe as the first argument, and either returns a (possibly modified) recipe or raises a custom exception. The remainder of the apply function can do arbitrarily complicated things, and will do so in the asyncio loop.

Subclasses of bioconda_utils.hosters.Hoster use regular expressions to detect which recipes came from which site and how to find links to new source code from that site. There are existing hosters for e.g. GitHub, PyPI, CRAN, CPAN, Bioconductor, and more. These are primarily used by the UpdateVersion filter.

Writing a filter

To add additional functionality to the scanner, create a filter in bioconda_utils.autobump and add it to the scanner in bioconda_utils.cli.autobump(). There are existing filters to exclude recipes based on blacklist or subrecipe status, update a recipe based on the latest version found by a Hoster (see below), update checksums, figure out where to load a recipe from (i.e. master branch or an existing PR), create a new PR, and more.

Filters can be arbitrarily complex, like any Python function. See the bioconda_utils.autobump source for examples when writing a new filter – ExcludeSubrecipe is a relatively simple one; UpdateVersion is more complex.

Writing a new hoster

A Hoster is a mechanism for scraping links of new releases off of a site and is the means by which we detect new releases. It works primarily by regular expressions.

A hoster is a subclass of bioconda_utils.hosters.Hoster and is configured by

  • a regex used to try matching with the existing source URL in the recipe

  • a format string that will create the URL of the releases website on which possibly many versions may be listed

  • a regex to match links from that page and extract their version

If you want a recipe to be updated automatically (and existing hosters aren’t cutting it), write a new Hoster class in bioconda_utils.hosters. Any new classes will automatically be picked up by the HosterMeta metaclass and will be used to scan against existing recipes.

Adding an attribute to the class with the suffix _pattern allows the regular expression stored in that attribute to be subsituted into other regular expressions using {placeholder} format placeholders.

Take, for example, the Bioconductor hoster which is commented here for explanation purposes:

class Bioconductor(HTMLHoster):
    """Matches R packages hosted at Bioconductor"""

    # This link pattern will be filled into the `url_pattern` below at the
    # {link} placeholder. It will also be used to search for links within
    # the HTML page defined below for `releases_format`.
    # The `version` and `ext` placeholders here are regexps defined in the Hoster
    # parent class -- basically, anything that looks reasonably like
    # a version number will match for `version` and any of the extensions
    # supported by conda will match for `ext`. See the source in
    # bioconda_utils.hosters.Hoster for details. Those (quite complex)
    # regexps will be filled in at these placeholders.
    link_pattern = r"/src/contrib/(?P<package>[^/]+)_{version}{ext}"

    # Bioconductor packages are stored at different locations on the
    # Bioconductor site depending on if they're a code package or a data
    # package (annotation or experiment). This will match any of them, and
    # will be filled in to the `url_pattern` below at the {section}
    # placeholder.
    section_pattern = r"/(bioc|data/annotation|data/experiment)"

    # This is the pattern that will be checked against a recipe's source
    # URLs to figure out if the recipe is a Bioconductor package. `section`
    # and `link` are filled in from above (and `link` was in turn filled in
    # recursively from `version` and `ext`)
    url_pattern = r"bioconductor.org/packages/(?P<bioc>[\d\.]+){section}{link}"

    # This is the HTML page containing releases for this package. It will
    # be filled in with # any other placeholders and then it will be
    # scraped for links that match `link_pattern` defined above.
    releases_format = "https://bioconductor.org/packages/{bioc}/bioc/html/{package}.html"

To tie this all together:

  • A Scanner is set up, the UpdateVersion filter is added and the asyncio loop starts.

  • The scanner checks all recipes. Because it has the UpdateVersion filter added, and because an UpdateVersion filter will check a recipe against all configured hosters, a Bioconductor recipe will match the above Hoster.url_pattern for the Bioconductor hoster.

  • The hoster object will go to the site specified by releases_format and scrape links that match Hoster.link_pattern.

  • The UpdateVersion filter will inspect those links found by the hoster, figure out which is the most recent, and see if the existing recipe is up-to-date. If a more recent link was found, use that and write the new recipe with the updated version and URL.

  • The scanner also has the UpdateChecksums filter added, but it is added after UpdateVersion. This filter will inspect the package, download it, and update the checksum in the recipe.

In practice, depending on the command-line argument provided (and therefore which filters were conditionally added) the scanner will do other things like exclude recipes, create a new branch or push a new pull request to GitHub for testing.

Updating recipes for a pinning change

For compatibility reasons, sometimes packages need to be built for specific versions of dependency (e.g., R, Python, or boost). The packages produced for a particular version of a dependency are said to be pinned to that dependency version.

Bioconda has project wide pinnings for many common dependencies, such as numpy, R, and boost. These pinnings allow for consistency between package and facilitates adding multiple packages to the same conda environment (due to not requiring differing boost versions, for example). These pinnings are largely inherited from the conda-forge project and Bioconda then uses a particular version of them.

On rare occasions these pinnings are updated (e.g., changing compiler versions, or updating the supported versions of Python) and many packages need to be updated project-wide to account for this. To facilitate such updates, bioconda-utils now has an update-pinning subcommand. To use this, first create a conda environment with bioconda-utils:

$ conda create -n bioconda-utils conda=4.5.11 python=3.6
$ source activate bioconda-utils
$ conda install -y git pip --file https://raw.githubusercontent.com/bioconda/bioconda-utils/master/bioconda_utils/bioconda_utils-requirements.txt
$ pip install git+https://github.com/bioconda/bioconda-utils

You then have an environment with the most recent version of bioconda-utils. Below we use deepTools as an example to show how to update a package and all of its dependencies as needed due to a change in pinnings. First ensure you’re in the bioconda-recipes repository and then:

$ git checkout -b rebuild_deeptools
$ bioconda-utils update-pinning --bump-only-python recipes/ config.yml --packages deeptools
Packages requiring the following:
  No build number change needed: 2
  A rebuild for a new python version: 0
  A build number increment: 17
$  git status
On branch rebuild_deeptools
Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git checkout -- <file>..." to discard changes in working directory)

    modified:   recipes/bcftools/1.2/meta.yaml
    modified:   recipes/bcftools/1.3.1/meta.yaml
    modified:   recipes/bcftools/1.3/meta.yaml
    modified:   recipes/bcftools/meta.yaml
    modified:   recipes/deeptools/meta.yaml
    modified:   recipes/htslib/meta.yaml
    modified:   recipes/libdeflate/meta.yaml
    modified:   recipes/py2bit/meta.yaml
    modified:   recipes/pybigwig/0.1.11/meta.yaml
    modified:   recipes/pybigwig/meta.yaml
    modified:   recipes/pysam/0.10.0/meta.yaml
    modified:   recipes/pysam/0.7.7/meta.yaml
    modified:   recipes/pysam/0.8.3/meta.yaml
    modified:   recipes/pysam/0.9.1/meta.yaml
    modified:   recipes/pysam/meta.yaml
    modified:   recipes/samtools/1.6/meta.yaml
    modified:   recipes/samtools/meta.yaml

This incremented the build number for the deepTools package as well as all of its dependencies. Note that the 2 dependencies not updated were skipped because it was determined that they were unaffected by the dependency change. In most cases the --bump-only-python should be used. This results in packages that simply need a rebuild due to a change in Python version to have their build numbers incremented. Such packages would eventually be built anyway, but this facilitates the update process.